What is the difference between episodic and knowledge graph memory in this skill?

Episodic memory stores full conversation narratives and decisions, while the knowledge graph (mcp__memory) stores structured entities, such as project roles, specific patterns, and relations between issues.

Does this skill work automatically with other Claude skills?

Yes, it is specifically designed to be called by the session-start and issue-driven-development skills to provide a foundation of context for autonomous tasks.

How does Memory Integration solve the context loss problem in Claude Code?

It implements a systematic approach to indexing conversation history through episodic memory and storing facts in a knowledge graph, allowing the AI to query past interactions.

What should I avoid storing in the memory-integration skill?

You should avoid storing trivial implementation details that are obvious from the code, temporary debugging notes, or speculative thoughts that didn't lead to a conclusion.

Memory Integration

Name: Memory Integration
Author: troykelly

bytroykelly

Productivity & Workflow

Synchronizes conversation history and structured knowledge graphs to provide persistent context across AI development sessions.

About

Memory Integration bridges the gap between disconnected Claude Code sessions by utilizing a dual-memory system. It combines episodic memory for searching past conversation history with a structured knowledge graph for tracking long-term architectural decisions, entities, and relationships. This skill ensures that your AI assistant retains crucial context about issue-driven development, project patterns, and past technical decisions, eliminating the 'blank slate' problem and streamlining complex, multi-day coding tasks by ensuring the AI always remembers the 'why' behind the code.

Key Features

Persistent storage for non-obvious problem solutions and discovered code patterns
Standardized protocols for session-start context gathering and session-end persistence
Dual-layer memory system utilizing episodic history and structured knowledge graphs
Automated entity and relationship mapping for complex architectural decisions
Semantic and precise search capabilities for issue-specific context retrieval
0 GitHub stars

Use Cases

Resuming complex GitHub issues across multiple work sessions with full historical context
Retrieving previously solved debugging strategies for recurring TypeScript or build errors
Documenting architectural decisions and their rationales within a structured knowledge graph

About

Key Features

Persistent storage for non-obvious problem solutions and discovered code patterns
Standardized protocols for session-start context gathering and session-end persistence
Dual-layer memory system utilizing episodic history and structured knowledge graphs
Automated entity and relationship mapping for complex architectural decisions
Semantic and precise search capabilities for issue-specific context retrieval
0 GitHub stars

Use Cases

Resuming complex GitHub issues across multiple work sessions with full historical context
Retrieving previously solved debugging strategies for recurring TypeScript or build errors
Documenting architectural decisions and their rationales within a structured knowledge graph